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In many practical areas of machine learning, such as explainability, feature selection, data valuation, ensemble pruning, and federated learning, measuring relevance and attribution of various gains is a crucial topic.
For example, one may wonder: How important is a certain feature in a machine learning model’s decisions? What is the value of a single data point? Which models in an ensemble are the most valuable? Specific ways have been used to solve these concerns in various sectors. Continue Reading
Paper: https://arxiv.org/pdf/2202.05594v1.pdf
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Hi, i am happy to share with you the edited version of cityscapes to foggy cityscapes dataset for unsupervised domain adaptation ready to be used =)
https://github.com/fpv-iplab/Cityscapes-FoggyCityscapes
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Yesss.... A first paper in Nature today: Magnetic control of tokamak plasmas through deep reinforcement learning. After the proteins folding breakthrough, Deepmind is tackling controlled fusion through deep reinforcement learning (DRL). With the long-term promise of abundant energy without greenhouse gas emissions. What a challenge! But Deemind's Google's folks, you are our heros! Do it again! A Wired popular article.
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Scale released an interesting blogpost recently, looks like anyone can just log in and start using Scale Rapid to get data labels. Also thought the use case with the anime CycleGAN was pretty cool and had some interesting results!
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Hi ML community,
we recently released all of our papers regarding ML / AI federation, scaling and multi-processing over our website: https://www.databloom.ai/science
Its free, and we are happy to answer questions. We are also the team behind Apache Wayang, if you want to contribute, we are also happy!
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Hi all,
I am interested in finding references to up-to-date evaluation methods for generative modeling in unsupervised tasks across different model types.
I am familiar with papers such as https://arxiv.org/abs/2002.09797, https://arxiv.org/abs/2102.08921, https://arxiv.org/abs/1806.00035, which are motivated by being unable to estimate the likelihood or a lower-bound on the likelihood when using GANs.
Suppose one hoped to compare the performance across GANs, flows, and VAEs in a particular scenario. Would one of the above references, or something similar, be an approach you would consider?
Also, say you ignore GANs and compare models such as flows, VAEs, and other latent variable models. Would you consider similar approaches? I understand these models involve likelihood estimation or estimating a lower bound on the likelihood, but considering it is a lower bound for some of these models, comparing these seems off.
I appreciate any help you can provide.
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The textbook is published in print format, but a pdf version (recent draft) is available as a pdf.
Link: https://probml.github.io/pml-book/book1.html
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This post is co-authored with Jan Paul Assendorp, Thomas Lietzow, Christopher Masch, Alexander Meinert, Dr. Lars Palzer, Jan Schillemans of SIGNAL IDUNA. At SIGNAL IDUNA, a large German insurer, we are currently reinventing ourselves with our transformation program VISION2023 to become even more customer oriented. Two aspects are central to this transformation: the reorganization of […]
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Real-time feedback helps drive learning. This is especially important for designing presentations, learning new languages, and strengthening other essential skills that are critical to succeed in today’s workplace. However, many students and lifelong learners lack access to effective face-to-face instruction to hone these skills. In addition, with the rapid adoption of remote learning, educators are […]
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Time series data is widely present in our lives. Stock prices, house prices, weather information, and sales data captured over time are just a few examples. As businesses increasingly look for new ways to gain meaningful insights from time-series data, the ability to visualize data and apply desired transformations are fundamental steps. However, time-series data […]
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Easily one of the most revolutionary technologies in recent times — Blockchain is slated to disrupt so many industries that its…
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We are surrounded by artificial intelligence (AI) and machine learning (ML). Both AI and machine learning have altered the way we live and…
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Jaguar Land Rover and NVIDIA are redefining modern luxury, infusing intelligence into the customer experience. As part of its Reimagine strategy, Jaguar Land Rover announced today that it will develop its upcoming vehicles on the full-stack NVIDIA DRIVE Hyperion 8 platform, with DRIVE Orin delivering a wide spectrum of active safety, automated driving and parking Read article >
The post Reimagining Modern Luxury: NVIDIA Announces Partnership with Jaguar Land Rover appeared first on The Official NVIDIA Blog.
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Stepping deeper into the era of exascale AI, Atos gave the first look at its next-generation high-performance computer. The BullSequana XH3000 combines Atos’ patented fourth-generation liquid-cooled HPC design with NVIDIA technologies to deliver both more performance and energy efficiency. Giving users a choice of Arm or x86 computing architectures, it will come in versions using Read article >
The post Atos Previews Energy-Efficient, AI-Augmented Hybrid Supercomputer appeared first on The Official NVIDIA Blog.
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Python is a duck typing language. It means the data types of variables can change as long as the syntax […]
The post Duck-typing, scope, and investigative functions in Python appeared first on Machine Learning Mastery.
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We live in a complex world that is full of data, and it’s getting even more full every day. In 2020, the world collectively created, captured, copied, and consumed nearly 64.2 zettabytes of data and by 2025 that figure is expected to more than double to 180 zettabytes. Increasingly, companies depend on this data to… Read More »Calling All Data Scientists: Data Observability Needs You
The post Calling All Data Scientists: Data Observability Needs You appeared first on Data Science Central.
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https://thereader.mitpress.mit.edu/the-staggering-ecological-impacts-of-computation-and-the-cloud/
This supports many of the points made in this: https://kv-emptypages.blogspot.com/2021/11/the-carbon-footprint-of-machine-learning.html
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https://github.com/JoaoLages/RATransformers
I have made a package to be able to use pretrained language models on structured data.
By changing self-attention to be relation aware, you are able to pass implicit relations within the input to the model.
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Hey r/ml! I thought people here might enjoy (or possibly have a great discussion about) the latest episode in the MLOps Podcast.
In this episode, I'm speaking with Laszlo Sragner about how data scientists can write better code, how it affects real-world ML projects, and how to build an ML team. We also talk about how to break down ML problems into smaller, more manageable tasks, and a bunch of other things.
You can watch it here: https://www.youtube.com/watch?v=mtwGV-x3nSM
or listen to it here, or read some of the Q&A.
Would love to open up a discussion – what are your best practices for improving code-craft in machine learning projects?
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Now get all official and unofficial code implementations of any AI/ML papers as you're browsing DuckDuckGo, Reddit, Google, Scholar, Arxiv, Twitter and more!
(if code not yet available, request it with 1-click as well!)
https://chrome.google.com/webstore/detail/aiml-papers-with-code-eve/aikkeehnlfpamidigaffhfmgbkdeheil
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This model classifies iris flowers among three species (Setosa, Versicolor or Virginica) based on the length and width measurements of the sepals and petals using Neural Designer
https://www.neuraldesigner.com/learning/examples/iris-flowers-classification
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At the latest UEFA Champions League Finals, one of the world’s most anticipated annual soccer events, pop stars Marshmello, Khalid and Selena Gomez shared the stage for a dazzling opening ceremony at Portugal’s third-largest football stadium — without ever stepping foot in it. The stunning video performance took place in a digital twin of the Read article >
The post Peak Performance: Production Studio Sets the Stage for Virtual Opening Ceremony at European Football Championship appeared first on The Official NVIDIA Blog.
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A new deep-learning algorithm trained to optimize doses of propofol to maintain unconsciousness during general anesthesia could augment patient monitoring.
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Internet of things (IoT) holds an enormous promise in making urban transport systems smarter in terms of safety, energy-saving, ecologically favorable, and efficiency. The efficiency of optimizing transportation in real-time is the key pillar of successful deployment of IoT. Ecosystem to Develop Pivoted on Expanding Use Cases Several developed nations notably Singapore, the U.S., and… Read More »IoT in Intelligent Transportation Systems Anchors Smart Traffic for Smart Cities
The post IoT in Intelligent Transportation Systems Anchors Smart Traffic for Smart Cities appeared first on Data Science Central.
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Amazon SageMaker Autopilot makes it possible for organizations to quickly build and deploy an end-to-end machine learning (ML) model and inference pipeline with just a few lines of code or even without any code at all with Amazon SageMaker Studio. Autopilot offloads the heavy lifting of configuring infrastructure and the time it takes to build […]
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Architecture evaluation is a systematic approach for identifying flaws and dangers in architectural designs. The evaluation process is ideally performed before they are implemented.
Typically, neural architecture search (NAS) systems are used for architectural evaluation. Neural architecture search (NAS) is an AutoML branch that aims to find the best deep-learning model architecture for a task. The systems achieve this by finding an architecture that will achieve the best performance metric on the given task dataset and search space of possible architectures. However, this usually necessitates training each proposed model completely on the dataset, which takes a long time. Continue Reading
Paper: http://proceedings.mlr.press/v139/xu21m/xu21m.pdf
Github: https://github.com/Jingjing-NLP/KNAS
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Datacenter accelerators are pieces of hardware that are specifically built to process visual data. It’s a physical device or software program that boosts a computer’s overall performance. Continuous advancements in creating and delivering data center (DC) machine learning (ML) accelerators, such as TPUs and GPUs, have proven crucial for scaling up contemporary ML models and applications. These upgraded accelerators’ ultimate performance (e.g., FLOPs) is orders of magnitude higher than that of standard computing systems.
However, there is a rapidly widening gap between the potential peak performance supplied by state-of-the-art hardware and the actual achievable performance when ML models run on these kinds of hardware. Continue Reading
Paper: https://openaccess.thecvf.com/content/CVPR2021/papers/Li\_Searching\_for\_Fast\_Model\_Families\_on\_Datacenter\_Accelerators\_CVPR\_2021\_paper.pdf
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Looking for a challenge? Try maneuvering a Kenyan minibus through traffic or dropping seed balls on deforested landscapes. Or download Africa’s Legends and battle through fiendishly difficult puzzles with Ghana’s Ananse or Nigeria’s Oya by your side. Games like these are connecting with a hyper-connected African youth population that’s growing fast. Africa is the youngest Read article >
The post New Levels Unlocked: Africa’s Game Developers Reach Toward the Next Generation appeared first on The Official NVIDIA Blog.
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Hey all!
We're building towards a GPT3 level moment in computer vision, and here's our v0 - https://youtu.be/P7zcc8iZ0YA
This v0 runs on 13B parameters, with 18B and 34B model iterations coming in the pipeline.
Access to the model is gated as of now to help us monitor scale, you can sign up at - https://banana-dev.typeform.com/carrot
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